`orthogene`: Interspecies gene mapping ================ <img src='https://github.com/neurogenomics/orthogene/raw/main/inst/hex/hex.png' title='Hex sticker for orthogene' height='300'><br> [](https://www.bioconductor.org/packages/orthogene) [](https://bioconductor.org/packages/stats/bioc/orthogene) [](https://bioconductor.org/packages/stats/bioc/orthogene) [](https://bioconductor.org/packages/stats/bioc/orthogene) [](https://cran.r-project.org/web/licenses/GPL-3) <br> [](https://github.com/neurogenomics/orthogene) [](https://github.com/neurogenomics/orthogene) [](https://github.com/neurogenomics/orthogene/commits/main) <br> [](https://github.com/neurogenomics/orthogene/actions) [](https://app.codecov.io/gh/neurogenomics/orthogene) <br> <a href='https://app.codecov.io/gh/neurogenomics/orthogene/tree/main' target='_blank'><img src='https://codecov.io/gh/neurogenomics/orthogene/branch/main/graphs/icicle.svg' title='Codecov icicle graph' width='200' height='50' style='vertical-align: top;'></a> <h4> Authors: <i>Brian Schilder</i> </h4> <h4> README updated: <i>Aug-30-2023</i> </h4> # Intro `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. In brief, `orthogene` lets you easily: - [**`convert_orthologs`** between any two species.](https://neurogenomics.github.io/orthogene/articles/orthogene#convert-orthologs) - [**`map_species`** names onto standard taxonomic ontologies.](https://neurogenomics.github.io/orthogene/articles/orthogene#map-species) - [**`report_orthologs`** between any two species.](https://neurogenomics.github.io/orthogene/articles/orthogene#report-orthologs) - [**`map_genes`** onto standard ontologies](https://neurogenomics.github.io/orthogene/articles/orthogene#map-genes) - [**`aggregate_mapped_genes`** in a matrix.](https://neurogenomics.github.io/orthogene/articles/orthogene#aggregate-mapped-genes) - [get **`all_genes`** from any species.](https://neurogenomics.github.io/orthogene/articles/orthogene#get-all-genes) - [**`infer_species`** from gene names.](https://neurogenomics.github.io/orthogene/articles/infer_species.html) - [**`create_background`** gene lists based one, two, or more species.](https://neurogenomics.github.io/orthogene/reference/create_background.html) - [**`get_silhouettes`** of each species from phylopic.](https://neurogenomics.github.io/orthogene/reference/get_silhouettes.html) - [**`prepare_tree`** with evolutionary divergence times across \>147,000 species.](https://neurogenomics.github.io/orthogene/reference/prepare_tree.html) ## Citation If you use `orthogene`, please cite: <!-- Modify this by editing the file: inst/CITATION --> > Brian M. Schilder, Nathan G. Skene (2022). orthogene: Interspecies > gene mapping. R package version 1.4.0, > <https://doi.org/doi:10.18129/B9.bioc.orthogene> ## [Documentation website](https://neurogenomics.github.io/orthogene/) ## [PDF manual](https://github.com/neurogenomics/orthogene/blob/main/inst/orthogene_1.5.1.pdf) # Installation ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") # orthogene is only available on Bioconductor>=3.14 if(BiocManager::version()<"3.14") BiocManager::install(update = TRUE, ask = FALSE) BiocManager::install("orthogene") ``` ## Docker `orthogene` can also be installed via a [Docker](https://hub.docker.com/repository/docker/neurogenomicslab/orthogene) or [Singularity](https://sylabs.io/guides/2.6/user-guide/singularity_and_docker.html) container with Rstudio pre-installed. Further [instructions provided here](https://neurogenomics.github.io/orthogene/articles/docker). # Methods ``` r library(orthogene) data("exp_mouse") # Setting to "homologene" for the purposes of quick demonstration. # We generally recommend using method="gprofiler" (default). method <- "homologene" ``` For most functions, `orthogene` lets users choose between different methods, each with complementary strengths and weaknesses: `"gprofiler"`, `"homologene"`, and `"babelgene"` In general, we recommend you use `"gprofiler"` when possible, as it tends to be more comprehensive. While `"babelgene"` contains less species, it queries a wide variety of orthology databases and can return a column “support_n” that tells you how many databases support each ortholog gene mapping. This can be helpful when you need a semi-quantitative measure of mapping quality. It’s also worth noting that for smaller gene sets, the speed difference between these methods becomes negligible. | | gprofiler | homologene | babelgene | |:--------------------|:------------------------------|:-------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Reference organisms | 700+ | 20+ | 19 (but cannot convert between pairs of non-human species) | | Gene mappings | More comprehensive | Less comprehensive | More comprehensive | | Updates | Frequent | Less frequent | Less frequent | | Orthology databases | Ensembl, HomoloGene, WormBase | HomoloGene | HGNC Comparison of Orthology Predictions (HCOP), which includes predictions from eggNOG, Ensembl Compara, HGNC, HomoloGene, Inparanoid, NCBI Gene Orthology, OMA, OrthoDB, OrthoMCL, Panther, PhylomeDB, TreeFam and ZFIN | | Data location | Remote | Local | Local | | Internet connection | Required | Not required | Not required | | Speed | Slower | Faster | Medium | # Quick example ## Convert orthologs [`convert_orthologs`](https://neurogenomics.github.io/orthogene/reference/convert_orthologs.html) is very flexible with what users can supply as `gene_df`, and can take a `data.frame`/`data.table`/`tibble`, (sparse) `matrix`, or `list`/`vector` containing genes. Genes, transcripts, proteins, SNPs, or genomic ranges will be recognised in most formats (HGNC, Ensembl, RefSeq, UniProt, etc.) and can even be a mixture of different formats. All genes will be mapped to gene symbols, unless specified otherwise with the `...` arguments (see `?orthogene::convert_orthologs` or [here](https://neurogenomics.github.io/orthogene/reference/convert_orthologs.html) for details). ### Note on non-1:1 orthologs A key feature of [`convert_orthologs`](https://neurogenomics.github.io/orthogene/reference/convert_orthologs.html) is that it handles the issue of genes with many-to-many mappings across species. This can occur due to evolutionary divergence, and the function of these genes tend to be less conserved and less translatable. Users can address this using different strategies via `non121_strategy=`. ``` r gene_df <- orthogene::convert_orthologs(gene_df = exp_mouse, gene_input = "rownames", gene_output = "rownames", input_species = "mouse", output_species = "human", non121_strategy = "drop_both_species", method = method) ``` ## Preparing gene_df. ## sparseMatrix format detected. ## Extracting genes from rownames. ## 15,259 genes extracted. ## Converting mouse ==> human orthologs using: homologene ## Retrieving all organisms available in homologene. ## Mapping species name: mouse ## Common name mapping found for mouse ## 1 organism identified from search: 10090 ## Retrieving all organisms available in homologene. ## Mapping species name: human ## Common name mapping found for human ## 1 organism identified from search: 9606 ## Checking for genes without orthologs in human. ## Extracting genes from input_gene. ## 13,416 genes extracted. ## Extracting genes from ortholog_gene. ## 13,416 genes extracted. ## Checking for genes without 1:1 orthologs. ## Dropping 46 genes that have multiple input_gene per ortholog_gene (many:1). ## Dropping 56 genes that have multiple ortholog_gene per input_gene (1:many). ## Filtering gene_df with gene_map ## Setting ortholog_gene to rownames. ## ## =========== REPORT SUMMARY =========== ## Total genes dropped after convert_orthologs : ## 2,016 / 15,259 (13%) ## Total genes remaining after convert_orthologs : ## 13,243 / 15,259 (87%) ``` r knitr::kable(as.matrix(head(gene_df))) ``` | | astrocytes_ependymal | endothelial-mural | interneurons | microglia | oligodendrocytes | pyramidal CA1 | pyramidal SS | |:---------|---------------------:|------------------:|-------------:|----------:|-----------------:|--------------:|-------------:| | TSPAN12 | 0.3303571 | 0.5872340 | 0.6413793 | 0.1428571 | 0.1207317 | 0.2864750 | 0.1453634 | | TSHZ1 | 0.4285714 | 0.4468085 | 1.1551724 | 0.4387755 | 0.3621951 | 0.0692226 | 0.8320802 | | ADAMTS15 | 0.0089286 | 0.0978723 | 0.2206897 | 0.0000000 | 0.0231707 | 0.0117146 | 0.0375940 | | CLDN12 | 0.2232143 | 0.1148936 | 0.5517241 | 0.0510204 | 0.2609756 | 0.4376997 | 0.6842105 | | RXFP1 | 0.0000000 | 0.0127660 | 0.2551724 | 0.0000000 | 0.0158537 | 0.0511182 | 0.0751880 | | SEMA3C | 0.1964286 | 0.9957447 | 8.6379310 | 0.2040816 | 0.1853659 | 0.1608094 | 0.2280702 | `convert_orthologs` is just one of the many useful functions in `orthogene`. Please see the [documentation website](https://neurogenomics.github.io/orthogene/articles/orthogene) for the full vignette. # Additional resources ## [Hex sticker creation](https://github.com/neurogenomics/orthogene/blob/main/inst/hex/hexSticker.Rmd) ## [Benchmarking methods](https://github.com/neurogenomics/orthogene/blob/main/inst/benchmark/benchmarks.Rmd) # Session Info <details> ``` r utils::sessionInfo() ``` ## R version 4.2.1 (2022-06-23) ## Platform: x86_64-apple-darwin17.0 (64-bit) ## Running under: macOS Big Sur ... 10.16 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] orthogene_1.7.1 ## ## loaded via a namespace (and not attached): ## [1] nlme_3.1-162 bitops_1.0-7 ## [3] ggtree_3.6.2 bit64_4.0.5 ## [5] filelock_1.0.2 RColorBrewer_1.1-3 ## [7] BiocPkgTools_1.16.1 httr_1.4.6 ## [9] rprojroot_2.0.3 gh_1.4.0 ## [11] tools_4.2.1 backports_1.4.1 ## [13] utf8_1.2.3 R6_2.5.1 ## [15] DT_0.28 lazyeval_0.2.2 ## [17] DBI_1.1.3 BiocGenerics_0.44.0 ## [19] colorspace_2.1-0 withr_2.5.0 ## [21] tidyselect_1.2.0 bit_4.0.5 ## [23] curl_5.0.0 compiler_4.2.1 ## [25] graph_1.76.0 cli_3.6.1 ## [27] rvest_1.0.3 Biobase_2.58.0 ## [29] xml2_1.3.4 plotly_4.10.2 ## [31] desc_1.4.2 scales_1.2.1 ## [33] readr_2.1.4 RBGL_1.74.0 ## [35] rappdirs_0.3.3 stringr_1.5.0 ## [37] digest_0.6.31 yulab.utils_0.0.6 ## [39] rmarkdown_2.22 rorcid_0.7.0 ## [41] pkgconfig_2.0.3 htmltools_0.5.5 ## [43] dbplyr_2.3.2 fastmap_1.1.1 ## [45] htmlwidgets_1.6.2 rlang_1.1.1 ## [47] rstudioapi_0.14 httpcode_0.3.0 ## [49] RSQLite_2.3.1 badger_0.2.3 ## [51] gridGraphics_0.5-1 generics_0.1.3 ## [53] jsonlite_1.8.4 dplyr_1.1.2 ## [55] car_3.1-2 RCurl_1.98-1.12 ## [57] homologene_1.4.68.19.3.27 magrittr_2.0.3 ## [59] ggplotify_0.1.0 patchwork_1.1.2 ## [61] fauxpas_0.5.2 Matrix_1.5-4.1 ## [63] Rcpp_1.0.10 munsell_0.5.0 ## [65] fansi_1.0.4 ape_5.7-1 ## [67] abind_1.4-5 babelgene_22.9 ## [69] lifecycle_1.0.3 stringi_1.7.12 ## [71] whisker_0.4.1 yaml_2.3.7 ## [73] carData_3.0-5 biocViews_1.66.3 ## [75] BiocFileCache_2.6.1 grid_4.2.1 ## [77] blob_1.2.4 parallel_4.2.1 ## [79] lattice_0.21-8 hms_1.1.3 ## [81] knitr_1.43 pillar_1.9.0 ## [83] igraph_1.5.0.1 ggpubr_0.6.0 ## [85] RUnit_0.4.32 ggsignif_0.6.4 ## [87] rworkflows_0.99.12 stats4_4.2.1 ## [89] gprofiler2_0.2.1 crul_1.4.0 ## [91] XML_3.99-0.14 glue_1.6.2 ## [93] evaluate_0.21 ggfun_0.0.9 ## [95] data.table_1.14.8 renv_0.17.3 ## [97] BiocManager_1.30.20 treeio_1.23.1 ## [99] vctrs_0.6.2 tzdb_0.4.0 ## [101] grr_0.9.5 gtable_0.3.3 ## [103] purrr_1.0.1 tidyr_1.3.0 ## [105] cachem_1.0.8 ggplot2_3.4.2 ## [107] xfun_0.39 broom_1.0.4 ## [109] tidytree_0.4.2 rstatix_0.7.2 ## [111] viridisLite_0.4.2 dlstats_0.1.7 ## [113] tibble_3.2.1 aplot_0.1.10 ## [115] rvcheck_0.2.1 memoise_2.0.1 ## [117] here_1.0.1 </details> # Related projects ## Tools - [`gprofiler2`](https://cran.r-project.org/web/packages/gprofiler2/vignettes/gprofiler2.html): `orthogene` uses this package. `gprofiler2::gorth()` pulls from [many orthology mapping databases](https://biit.cs.ut.ee/gprofiler/page/organism-list). - [`homologene`](https://github.com/oganm/homologene): `orthogene` uses this package. Provides API access to NCBI [HomoloGene](https://www.ncbi.nlm.nih.gov/homologene) database. - [`babelgene`](https://cran.r-project.org/web/packages/babelgene/vignettes/babelgene-intro.html): `orthogene` uses this package. `babelgene::orthologs()` pulls from [many orthology mapping databases](https://cran.r-project.org/web/packages/babelgene/vignettes/babelgene-intro.html). - [`annotationTools`](https://www.bioconductor.org/packages/release/bioc/html/annotationTools.html): For interspecies microarray data. - [`orthology`](https://www.leibniz-hki.de/en/orthology-r-package.html): R package for ortholog mapping (deprecated?). - [`hpgltools::load_biomart_orthologs()`](https://rdrr.io/github/elsayed-lab/hpgltools/man/load_biomart_orthologs.html): Helper function to get orthologs from biomart. - [`JustOrthologs`](https://github.com/ridgelab/JustOrthologs/): Ortholog inference from multi-species genomic sequences. - [`orthologr`](https://github.com/drostlab/orthologr): Ortholog inference from multi-species genomic sequences. - [`OrthoFinder`](https://github.com/davidemms/OrthoFinder): Gene duplication event inference from multi-species genomics. ## Databases - [HomoloGene](https://www.ncbi.nlm.nih.gov/homologene): NCBI database that the R package [homologene](https://github.com/oganm/homologene) pulls from. - [gProfiler](https://biit.cs.ut.ee/gprofiler): Web server for functional enrichment analysis and conversions of gene lists. - [OrtholoGene](http://orthologene.org/resources.html): Compiled list of gene orthology resources. ## Contact ### [Neurogenomics Lab](https://www.neurogenomics.co.uk/) UK Dementia Research Institute Department of Brain Sciences Faculty of Medicine Imperial College London [GitHub](https://github.com/neurogenomics) [DockerHub](https://hub.docker.com/orgs/neurogenomicslab) <br>